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Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. Furthermore, satellite and sky camera based methods lose resolution at a 1-h time horizon. Exponential smoothing (ETS) has received great attention in the recent years due to the invention of its state space formulation. We explore 1-h ahead ETS forecasting of solar irradiance in this paper. Several knowledge based decompositions are considered to improve the forecast accuracy and computation speed.Three methods are proposed. The first method considers an additive seasonal-trend decomposition prior to the use of ETS. In such a way, the ETS state space is reduced, thus facilitates online forecasting applications. The second method decomposes the global horizontal irradiance (GHI) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; the results are recombined through the closure equation. We consider the time series of cloud cover index in the third method. ETS is first applied to the cloud cover time series to produce the forecast. The forecast cloud cover is then used to reconstruct GHI through regressions. It is found that the third method performs the best among three methods and all proposed methods outperforms persistence.

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Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number (R0) of 2.2−2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to screen for COVID-19 using artificial intelligence. Our technique takes only seconds to screen for the presence of the virus in a patient. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify the chest X-rays. Unsatisfied with these models, we then designed and built a Residual Attention Network that was able to screen COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A feature maps visual of our model show areas in a chest X-ray which are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice. The code and dataset used in this project are available at https://github.com/vishalshar/ covid-19-screening-using-RAN-on-X-ray-images.

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The goal of this research is to learn about whole farm carbon models. A whole farm carbon model estimates the emissions of greenhouse gasses (GHGs) based on information for a farm. We analyzed two models, HOLOS whole-farm and COMET-Farm, by running the models on random inputs and building classifiers from the runs. HOLOS estimates GHG emissions for a particular year based on crop and animal agriculture input, while COMET-farm adds past and future farm management practices. Users of the models must manually enter farm data through a graphical user interface (GUI), which is a good method for a single farm, but makes it infeasible to calculate GHG emissions over hundreds to thousands of farms. So we automated the interface and generated random farm scenarios within ranges given by experts. We scraped the estimated carbon footprint from thousands of runs of the models and used several Regression algorithms to build predictive models that have high accuracy. By reverse engineering the whole-farm carbon models we were able to determine which farm management practices in each whole farm carbon model have the biggest impact on GHG emissions. This can help farmers and rural planners change farm management practices to decrease GHG emissions.